CN109993311A - The analysis method of knowledge learning - Google Patents
The analysis method of knowledge learning Download PDFInfo
- Publication number
- CN109993311A CN109993311A CN201711460503.7A CN201711460503A CN109993311A CN 109993311 A CN109993311 A CN 109993311A CN 201711460503 A CN201711460503 A CN 201711460503A CN 109993311 A CN109993311 A CN 109993311A
- Authority
- CN
- China
- Prior art keywords
- knowledge
- user
- practice
- hot spot
- topic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides a kind of analysis method of knowledge learning, comprising: obtains and whether the log-on message for verifying user is qualified；When logon information qualification, judge whether the user participates in knowledge practice for the first time, is practiced according to the corresponding knowledge of judgment result displays；It analyzes all practice results in the previous historical record of the user and obtains corresponding knowledge hot spot；Make the self-teaching of user's progress next time using the topic that the knowledge hot spot updates the knowledge practice.By in the previous historical record of traverse user, detecting the knowledge hot spot of the corresponding user during user practices, knowledge hot spot is updated to the subsequent knowledge practice topic of user and is practiced, on the one hand, the efficiency that user's study can be improved, achievees the effect that leakage detection is filled a vacancy；On the other hand, inherently targetedly learn and understand convenient for user according to the type of each user knowledge hot spot in big data.
Description
Technical field
The present invention relates to knowledge learning technical fields, more particularly to a kind of analysis method of knowledge learning.
Background technique
In informationized society, people have more visitors or business than one can attend in face of the data information of magnanimity, needed for how learning, grasping oneself
The information wanted has become the focal issue for Information Technology Development and internet development.
To solve user's above problem, primarily now there is following two way: first, to solve how to obtain, learn oneself
Information required for oneself facilitates user search knowledge using knowledge classification；Second, to solve how to grasp oneself knowledge,
Practiced using knowledge, the methods of recommendation of relevant knowledge.The knowledge analysis of central issue of self-teaching refers to study, practice or explores
It is obtained understanding, judgement or technical ability further understand and grasp by way of self-teaching, collect user behavior, with point
It analyses algorithm and analyzes user behavior, obtain knowledge hot spot.
However, the parser of this self-teaching at present conveniently and efficiently allows user's working knowledge, but also only transport
It uses in practice knowledge, will not practice the behavior generated during knowledge by the parser of big data and generate knowledge
Hot spot can apply to other aspects of user, the problem for causing user's self-teaching inefficient.
Summary of the invention
In view of the foregoing deficiencies of prior art, the purpose of the present invention is to provide a kind of analysis sides of knowledge learning
Method, for solving the problems, such as that user's self-teaching is inefficient in the prior art.
In order to achieve the above objects and other related objects, the application's in a first aspect, present invention provides a kind of knowledge learning
Analysis method, comprising:
It obtains and whether the log-on message for verifying user is qualified；
Judge whether the user participates in knowledge practice for the first time, is practiced according to the corresponding knowledge of judgment result displays；
It analyzes all practice results in the previous historical record of the user and obtains corresponding knowledge hot spot；
Make the self-teaching of user's progress next time using the topic that the knowledge hot spot updates the knowledge practice.
As described above, the analysis method of knowledge learning of the invention, has the advantages that
By in the previous historical record of traverse user, detecting the knowledge of the corresponding user during user practices
Knowledge hot spot is updated to the subsequent knowledge practice topic of user and is practiced, on the one hand, user's study can be improved by hot spot
Efficiency achievees the effect that leakage detection is filled a vacancy；On the other hand, according to the type of each user knowledge hot spot in big data, convenient for using
Family inherently targetedly learns and understands.
Detailed description of the invention
Fig. 1 is shown as the present invention and provides a kind of analysis method flow chart of knowledge learning；
Fig. 2 is shown as step S2 flow chart in a kind of analysis method of knowledge learning of the present invention；
Fig. 3 is shown as step S3 flow chart in a kind of analysis method of knowledge learning of the present invention.
Specific embodiment
Presently filed embodiment is illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book understands other advantages and effect of the application easily.
In described below, with reference to attached drawing, attached drawing describes several embodiments of the application.It should be appreciated that also can be used
Other embodiments, and can be carried out without departing substantially from spirit and scope of the present disclosure mechanical composition, structure, electrically with
And the operational detailed description changed below should not be considered limiting, and the range of embodiments herein
Only the limited of claims of the patent by announcing term used herein is merely to describe specific embodiment, and be not
It is intended to limit the application.The term of space correlation, for example, "upper", "lower", "left", "right", " following ", " lower section ", " lower part ",
" top ", " top " etc. can be used in the text in order to an elements or features and another element or spy shown in explanatory diagram
The relationship of sign.
Although term first, second etc. are used to describe various elements herein in some instances, these elements
It should not be limited by these terms.These terms are only used to distinguish an element with another element.For example, first is pre-
If threshold value can be referred to as the second preset threshold, and similarly, the second preset threshold can be referred to as the first preset threshold, and
The range of various described embodiments is not departed from.First preset threshold and preset threshold are to describe a threshold value, still
Unless context otherwise explicitly points out, otherwise they are not the same preset thresholds.Similar situation further includes first
Volume and the second volume.
Furthermore as used in herein, singular " one ", "one" and "the" are intended to also include plural number shape
Formula, unless having opposite instruction in context it will be further understood that term "comprising", " comprising " show that there are the spies
Sign, step, operation, element, component, project, type, and/or group, but it is not excluded for one or more other features, step, behaviour
Work, element, component, project, the presence of type, and/or group, appearance or addition term "or" used herein and "and/or" quilt
Be construed to inclusive, or mean any one or any combination therefore, " A, B or C " or " A, B and/or C " mean " with
Descend any one: A；B；C；A and B；A and C；B and C；A, B and C " is only when element, function, step or the combination of operation are in certain modes
Under it is inherently mutually exclusive when, just will appear the exception of this definition.
The application provides a kind of screen rotation control system, method and setting, suitable for electronic equipment, in actual reality
Apply in mode, the electronic equipment be, for example, include but is not limited to laptop, tablet computer, mobile phone, smart phone,
Media player, personal digital assistant (PDA), navigator, smart television, smartwatch, digital camera etc., further include wherein
Two or multinomial combinations.It should be appreciated that the application electronic equipment described in embodiment is an application example, it should
The component of equipment can have more or fewer components than diagram, or with different component Configurations.Draw each of diagram
Kind of component can realize with the combination of hardware, software or software and hardware, including one or more signal processings and/or dedicated integrated
Circuit.In a specific embodiment of the present application, it will be illustrated so that the electronic equipment is smart phone as an example.
Referring to Fig. 1, the present invention provides a kind of analysis method flow chart of knowledge learning, comprising:
Step S1, obtains and whether the log-on message for verifying user is qualified；
Wherein, it obtains the user login information and whether verify the log-on message qualified；When the login of the user
When information is identical as the user information prestored, then qualification is verified；When the user log-on message with the user information that prestores not
Meanwhile then authentication failed.It protects user information not usurped by stranger by verifying, otherwise, knows for what user recommended
Know the purpose that hot spot is unable to reach high-efficiency learning.
Step S2 judges whether the user participates in knowledge practice for the first time when logon information qualification, according to judging result
Show corresponding knowledge practice；
Wherein, by judging whether user is that initial knowledge of participating in is practiced, user is treated with a certain discrimination, on the one hand, realize big
Accurate data alignment, guarantees that the knowledge hot spot of each user corresponds to each user, improves its utilization rate；On the other hand, if
User carries out knowledge practice for the first time, just carries out recommendation according to the knowledge hot spot in large database concept and shows.
Step S3 analyzes all practice results in the previous historical record of the user and obtains corresponding knowledge hot spot；
Wherein, the corresponding historical record for having the user is just only understood when user is not to participate in knowledge practice for the first time,
Corresponding knowledge hot spot is obtained, otherwise, no historical record is unable to get corresponding knowledge hot spot.
Step S4 makes self of user's progress next time using the topic that the knowledge hot spot updates the knowledge practice
It practises.
It is constantly updated using knowledge hot spot, is constantly accumulated, the knowledge for updating corresponding user practices topic, to allow user's needle
Knowledge practice to property, makes user grasp more knowledge within the shorter time, improves the efficiency of knowledge learning.
In the present embodiment, by the previous historical record of traverse user, detecting correspondence during user practices
Knowledge hot spot is updated to the subsequent knowledge practice topic of user and is practiced, on the one hand, can be improved by the knowledge hot spot of the user
The efficiency of user's study, achievees the effect that leakage detection is filled a vacancy；On the other hand, according to each user knowledge hot spot in big data
Type inherently targetedly learns and understands convenient for user.
Referring to Fig. 2, for step S2 flow chart in a kind of analysis method of knowledge learning, comprising:
Step S201, the account number logged according to the user detect whether user corresponding to the account number is to participate in for the first time
Knowledge practice；
Specifically, detection user whether for the first time participate in knowledge practice, can by the historical record corresponding to account, or
Person, the modes such as registion time of account are verified.
The topic of knowledge practice is randomly generated when the user is first participation knowledge practice in step S202；
Specifically, due to not knowing that user currently grasps the level of knowledge, practice topic can only be generated at random and is practiced for user
It practises, difficulty, the knowledge point of knowledge topic etc. of practice is moderately adjusted further according to the practice conditions of user.
Step S203, when the user is not first participation knowledge practice, the user stored in analytical database is previous
Practice conditions obtain corresponding knowledge hot spot in historical record.
Specifically, the previous historical record of user obtains the knowledge hot spot for being suitble to user in analytical database, due to difference
Levels of user sophistication or knowledge learning ability difference cause when practicing knowledge topic, it is different to, wrong topic affirmative,
Therefore, finally the knowledge hot spot of corresponding user is also different.
In the present embodiment, since each user is specifically corresponding with its knowledge hot spot needed to be grasped, it can
Knowledge is divided by type, user is facilitated to grasp and learn as soon as possible.
Referring to Fig. 3, for step S3 flow chart in a kind of analysis method of knowledge learning, comprising:
Step S301 obtains the corresponding historical record of the user knowledge practice；
Specifically, backstage directly grabs historical record corresponding to the user.
Step S302 traverses the historical record and detects the practice topic which item in the historical record is recorded as mistake；
Specifically, it is not operated if the corresponding practice topic of correct record.
The malpractice topic is put into the knowledge base for needing to practice next time by step S303, and by the mistake
Accidentally practice topic is put into malpractice array；
Step S304 after the detection and judges the practice time for occurring same error in the malpractice array
Whether number is more than preset threshold；
Step S3041, if the practice number for occurring same error in the malpractice array is more than preset threshold,
Then topic or knowledge point corresponding to the practice are put into knowledge hot spot；
Step S3042, if the practice number for occurring same error in the malpractice array is less than preset threshold
When, then it does not operate.
It in the present embodiment, is all the desired knowledge hot spot of user due to not being per wrong topic together, therefore, it is necessary to right
Knowledge hot spot is judged, during practice, prevents user because fault (reasons such as clerical mistake, dim eyesight, absent-minded) leads to topic
Practice mistake, setting preset threshold is screened in the malpractice array corresponding to the topic of mistake, improves knowledge hot spot
The accuracy rate of screening.
In conclusion the present invention is by during user practices, in the previous historical record of traverse user, detect pair
Should user knowledge hot spot, knowledge hot spot is updated to user's subsequent knowledge practice topic and is practiced, on the one hand, can be mentioned
The high efficiency of user's study, achievees the effect that leakage detection is filled a vacancy；On the other hand, according to each user knowledge hot spot in big data
Type, convenient for user inherently targetedly learn and understand.So the present invention effectively overcomes in the prior art kind
It plants disadvantage and has high industrial utilization value.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe
The personage for knowing this technology all without departing from the spirit and scope of the present invention, carries out modifications and changes to above-described embodiment.Cause
This, institute is complete without departing from the spirit and technical ideas disclosed in the present invention by those of ordinary skill in the art such as
At all equivalent modifications or change, should be covered by the claims of the present invention.
Claims (5)
1. a kind of analysis method of knowledge learning characterized by comprising
It obtains and whether the log-on message for verifying user is qualified；
When logon information qualification, judge whether the user participates in knowledge practice for the first time, it is corresponding according to judgment result displays
Knowledge practice；
It analyzes all practice results in the previous historical record of the user and obtains corresponding knowledge hot spot；
Make the self-teaching of user's progress next time using the topic that the knowledge hot spot updates the knowledge practice.
2. the analysis method of knowledge learning according to claim 1, which is characterized in that the log-on message for obtaining user
The step of, comprising:
It obtains the user login information and whether verify the log-on message qualified；When the log-on message of the user with prestore
User information it is identical when, then verify qualification；When the log-on message of the user and the user information difference prestored, then verify
Failure.
3. the analysis method of knowledge learning according to claim 1, which is characterized in that it is described when logon information qualification,
The step of whether user participates in knowledge practice for the first time, practice according to the corresponding knowledge of judgment result displays judged, comprising:
Detect whether user corresponding to the account number is that first knowledge of participating in is practiced according to the account number that the user logs in；
When the user is first participation knowledge practice, the topic of knowledge practice is randomly generated；
When the user be not it is first participate in knowledge practice when, practice in the previous historical record of user that stores in analytical database
Situation obtains corresponding knowledge hot spot.
4. the analysis method of knowledge learning according to claim 1, which is characterized in that the analysis previous history of the user
All practice results obtain the step of corresponding knowledge hot spot in record, comprising:
Obtain the corresponding historical record of the user knowledge practice；
It traverses the historical record and detects the practice topic which item in the historical record is recorded as mistake；
The malpractice topic is put into the knowledge base for needing to practice next time, and the malpractice topic is put into
In malpractice array；
After the detection and whether the practice number that judges to occur same error in the malpractice array is more than pre-
If threshold value, if the practice number for occurring same error in the malpractice array is more than preset threshold, the practice institute
Corresponding topic or knowledge point are put into knowledge hot spot；If occurring the practice number of same error in the malpractice array not
When more than preset threshold, then do not operate.
5. the analysis method of knowledge learning according to claim 1, which is characterized in that described to utilize the knowledge hot spot more
The step of topic of the new knowledge practice makes user carry out self-teaching next time, comprising:
With the practice situation real-time update knowledge hot spot of user, knowledge hot spot corresponding to user is added to subsequent knowledge
The topic of user knowledge practice is converted in practice.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711460503.7A CN109993311A (en) | 2017-12-28 | 2017-12-28 | The analysis method of knowledge learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711460503.7A CN109993311A (en) | 2017-12-28 | 2017-12-28 | The analysis method of knowledge learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109993311A true CN109993311A (en) | 2019-07-09 |
Family
ID=67108302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711460503.7A Pending CN109993311A (en) | 2017-12-28 | 2017-12-28 | The analysis method of knowledge learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109993311A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112235333A (en) * | 2019-07-15 | 2021-01-15 | 北京字节跳动网络技术有限公司 | Function package management method, device, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8903756B2 (en) * | 2007-10-19 | 2014-12-02 | Ying Zhao | System and method for knowledge pattern search from networked agents |
| CN104318497A (en) * | 2014-05-20 | 2015-01-28 | 鲁帆 | Method and system for automatic communitization learning |
| CN104835087A (en) * | 2015-04-30 | 2015-08-12 | 泸州市金点教育科技有限公司 | Data processing method and apparatus for education test system |
| CN104966427A (en) * | 2015-05-27 | 2015-10-07 | 北京创数教育科技发展有限公司 | Self-adaptation teaching interaction system and method |
| CN105528931A (en) * | 2016-01-18 | 2016-04-27 | 浙江工商大学 | Stage-accumulation-type exercise database construction method and system based on student participation in SPOC platform |
| CN106202453A (en) * | 2016-07-13 | 2016-12-07 | 网易（杭州）网络有限公司 | A kind of multimedia resource recommends method and apparatus |
| CN106547815A (en) * | 2016-09-23 | 2017-03-29 | 厦门市杜若科技有限公司 | A kind of specific aim operation generation method and system based on big data |
-
2017
- 2017-12-28 CN CN201711460503.7A patent/CN109993311A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8903756B2 (en) * | 2007-10-19 | 2014-12-02 | Ying Zhao | System and method for knowledge pattern search from networked agents |
| CN104318497A (en) * | 2014-05-20 | 2015-01-28 | 鲁帆 | Method and system for automatic communitization learning |
| CN104835087A (en) * | 2015-04-30 | 2015-08-12 | 泸州市金点教育科技有限公司 | Data processing method and apparatus for education test system |
| CN104966427A (en) * | 2015-05-27 | 2015-10-07 | 北京创数教育科技发展有限公司 | Self-adaptation teaching interaction system and method |
| CN105528931A (en) * | 2016-01-18 | 2016-04-27 | 浙江工商大学 | Stage-accumulation-type exercise database construction method and system based on student participation in SPOC platform |
| CN106202453A (en) * | 2016-07-13 | 2016-12-07 | 网易（杭州）网络有限公司 | A kind of multimedia resource recommends method and apparatus |
| CN106547815A (en) * | 2016-09-23 | 2017-03-29 | 厦门市杜若科技有限公司 | A kind of specific aim operation generation method and system based on big data |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112235333A (en) * | 2019-07-15 | 2021-01-15 | 北京字节跳动网络技术有限公司 | Function package management method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kassab et al. | A systematic literature review on Internet of things in education: Benefits and challenges | |
| Chai et al. | Changing teachers’ TPACK and design beliefs through the Scaffolded TPACK Lesson Design Model (STLDM) | |
| US10242500B2 (en) | Virtual reality based interactive learning | |
| CN108200030A (en) | Detection method, system, device and the computer readable storage medium of malicious traffic stream | |
| Belk et al. | The interplay between humans, technology and user authentication: A cognitive processing perspective | |
| CN104954131B (en) | The verification method and system of identifying code | |
| CN108230203A (en) | Learning interaction group construction method and system | |
| Cetto et al. | Friend inspector: A serious game to enhance privacy awareness in social networks | |
| US10839708B2 (en) | Computer-implemented system and method for administering an examination | |
| US10410534B2 (en) | Modular system for the real time assessment of critical thinking skills | |
| EP4339875A1 (en) | Career aspiration-based educational method and system | |
| Kayem | Graphical Passwords--A Discussion | |
| CN109636218A (en) | Learning content recommendation method and electronic equipment | |
| CN112698895A (en) | Display method, device, equipment and medium of electronic equipment | |
| Daimi et al. | Innovations in cybersecurity education | |
| CN109993311A (en) | The analysis method of knowledge learning | |
| Gao et al. | Analysis and evaluation of the colorlogin graphical password scheme | |
| Nasir | Exploring the effectiveness of cybersecurity training programs: factors, best practices, and future directions | |
| Cai et al. | Cybersecurity should be taught top-down and case-driven | |
| KR20150045974A (en) | Method and apparatus for providing learning contents | |
| CN108140329A (en) | Information processing equipment, information processing method and program | |
| KR101969085B1 (en) | System and method for learning foreign languages | |
| CN109978310A (en) | The analysis system of knowledge learning | |
| Arianezhad et al. | Usability and security of gaze-based graphical grid passwords | |
| Trahms et al. | Estimating quality ratings from touch interactions in mobile games |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190709 |